Steganography is the study of data hiding for the purpose of covert communication. A secret message is inserted into a cover file so that the very existence of the message is not apparent. Most current steganography algorithms insert data in the spatial or transform domains; common transforms include the discrete cosine transform, the discrete Fourier transform, and discrete wavelet transform. ...

Cerebral small vessel disease (SVD) is an important cause of stroke, cognitive decline and vascular dementia (VaD). It is associated with diffuse white matter abnormalities and small deep cerebral ischemic infarcts. The molecular mechanisms involved in the development and progression of SVD are unclear. As hypertension is a major risk factor for developing SVD, Spontaneously Hypertensive Rats (...

The present study was designed to explore the correlation between serum S100β levels and cognitive dysfunction in patients with cerebral small vessel disease (SVD). A total of 172 SVD patients participated in the study, and they were assigned to patients with no cognitive impairment (NCI group) and those with vascular cognitive impairment no dementia (VCIND group). In total, 105 people were rec...

An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that have many of the “blob-like” and “spiky” clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of ...

Patients with somatoform vertigo and dizziness (SVD) disorders often report instability of stance or gait and fear of falling. Posturographic measurements indeed indicated a pathological postural strategy. Our goal was to evaluate the effectiveness of a psychotherapeutic and psychoeducational short-term intervention (PTI) using static posturography and psychometric examination. Seventeen SVD pa...

The famous Singular Value Decomposition (SVD) is very efficient in the processing of multidimensional images, when efficient compression, and reduction of the features, used for objects recognition, are needed. The basic obstacle for the wide use of SVD is its high computational complexity. To solve the problem, here is offered the new approach for hierarchical image decomposition through SVD (...

OBJECTIVE To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. METHODS A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structu...

In this paper, the performance of SVD and Schur decomposition is evaluated and compared for image copyright protection applications. The watermark image is embedded in the cover image by using Quantization Index Modulus Modulation (QIMM) and Quantization Index Modulation (QIM). Watermark image is embedded in the D matrix of Schur decomposition and Singular Value Decomposition (SVD). Watermarkin...

This paper presents a dynamic image approach to characterize the growth of brain cancer invasion of tumor gliomas cells using singular value decomposition (SVD) technique. Such a dynamic image is identified by the white and grey matter displayed by magnetic resonance (MR) images of the patient brain taken at different times. SVD components and properties have been analyzed for different brain i...

The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, highdimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order ...